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pro vyhledávání: '"Asgharian, A"'
Outlying observations are frequently encountered in a wide spectrum of scientific domains, posing significant challenges for the generalizability of statistical models and the reproducibility of downstream analysis. These observations can be identifi
Externí odkaz:
http://arxiv.org/abs/2412.02945
Autor:
Asgharian-Sardroud, Asghar, Izanlou, Mohammad Hossein, Jabbari, Amin, Hamedani, Sepehr Mahmoodian
Network function virtualization enables network operators to implement new services through a process called service function chain mapping. The concept of Service Function Chain (SFC) is introduced to provide complex services, which is an ordered se
Externí odkaz:
http://arxiv.org/abs/2411.07606
In this paper, we introduce fastHDMI, a Python package designed for efficient variable screening in high-dimensional datasets, particularly neuroimaging data. This work pioneers the application of three mutual information estimation methods for neuro
Externí odkaz:
http://arxiv.org/abs/2410.10082
Autor:
Edalati, Ali, Ghaffari, Alireza, Asgharian, Masoud, Hou, Lu, Chen, Boxing, Nia, Vahid Partovi
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy required for their inference. Post-training Quantization (PTQ) techni
Externí odkaz:
http://arxiv.org/abs/2405.15025
The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Quantization (PTQ) often require calibration to achieve the desired accu
Externí odkaz:
http://arxiv.org/abs/2405.13358
Neuroimaging data allows researchers to model the relationship between multivariate patterns of brain activity and outcomes related to mental states and behaviors. However, the existence of outlying participants can potentially undermine the generali
Externí odkaz:
http://arxiv.org/abs/2401.13208
Autor:
Ghaffari, Alireza, Yu, Justin, Nejad, Mahsa Ghazvini, Asgharian, Masoud, Chen, Boxing, Nia, Vahid Partovi
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. However, this approach is susceptible to the existence of outlier values in
Externí odkaz:
http://arxiv.org/abs/2312.09211
Autor:
Alex Y. Ge, Abolfazl Arab, Raymond Dai, Albertas Navickas, Lisa Fish, Kristle Garcia, Hosseinali Asgharian, Jackson Goudreau, Sean Lee, Kathryn Keenan, Melissa B. Pappalardi, Michael T. McCabe, Laralynne Przybyla, Hani Goodarzi, Luke A. Gilbert
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Externí odkaz:
https://doaj.org/article/502044bc11f6478ab5fb48123fb6c155
Autor:
Nia, Vahid Partovi, Zhang, Guojun, Kobyzev, Ivan, Metel, Michael R., Li, Xinlin, Sun, Ke, Hemati, Sobhan, Asgharian, Masoud, Kong, Linglong, Liu, Wulong, Chen, Boxing
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computer
Externí odkaz:
http://arxiv.org/abs/2303.15464
With the rising complexity of numerous novel applications that serve our modern society comes the strong need to design efficient computing platforms. Designing efficient hardware is, however, a complex multi-objective problem that deals with multipl
Externí odkaz:
http://arxiv.org/abs/2303.08054